Reservoir modeling and simulation are commonly used in the oil & gas industry to model the structure and/or properties of a subsurface formation, e.g., of the type containing recoverable hydrocarbons, as well as to model the flow of fluids such as recoverable hydrocarbons throughout such a formation. Reservoir modeling and simulation may be used during various phases of exploration and production, including, for example, to attempt to predict the location, quantity and/or value of recoverable hydrocarbons, to plan the development of wells for cost-effectively extracting hydrocarbons from the subsurface formation, and to guide future and/or ongoing production and development decisions.
Many subsurface formations include some degree of fracturing, i.e., the presence of faults, joints, cracks and other discontinuities that separate rock within the subsurface formation. Fractures generally have greater permeability and porosity than solid rock, so accounting for the effects of fractures is generally desirable for accurate fluid flow simulation. In this regard, a number of different fracture abundance measures have been proposed to represent the relative amount of fracturing within a subsurface formation, including, for example, fracture density, fracture intensity, fracture porosity, etc. Some conventional approaches, for example, calculate a fracture density as a P10 value (number of fractures per unit length along a scanline) from wells. In addition, in some approaches a P32 value (sum of fracture area per unit volume) is inferred from the P10 value by making an assumption that fractures entirely intersect a borehole as well as corrected from borehole deviation and then using a statistical method to interpolate P32 in a three-dimensional (3D) grid as an input for Discrete Fracture Network (DFN) generation.
The P32 value is desirable in many applications because fracture size is accounted for in the value and does not depend on borehole trajectory. However, accurate fracture sizes within a borehole are generally difficult to obtain from borehole images and core logging, and generally result in the calculation of only a “relative” P32 measurement from wells. Furthermore, interpolation of this measurement generally creates large uncertainties within the 3D grid that generally cannot be easily quantified.
Therefore, a need exists in the art for improved evaluation of P32 and other fracture abundance parameters, and in particular, an improved evaluation having greater accuracy and/or greater computational efficiency than convention approaches.